






import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()






def 							lowerCamelCase__ (       __lowerCamelCase     :	str    ,						__lowerCamelCase     :	str    ,						__lowerCamelCase     :	str						):
  def get_masked_lm_array(__lowerCamelCase     :	str						):
    __UpperCAmelCase				:      str	 =    f"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
    __UpperCAmelCase				:      Optional[int]	 =    tf.train.load_variable(__lowerCamelCase    ,						__lowerCamelCase						)

    if "kernel" in name:
      __UpperCAmelCase				:      Optional[int]	 =    array.transpose()

    return torch.from_numpy(__lowerCamelCase						)

  def get_encoder_array(__lowerCamelCase     :	str						):
    __UpperCAmelCase				:      Optional[int]	 =    f"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
    __UpperCAmelCase				:      Union[str, Any]	 =    tf.train.load_variable(__lowerCamelCase    ,						__lowerCamelCase						)

    if "kernel" in name:
      __UpperCAmelCase				:      int	 =    array.transpose()

    return torch.from_numpy(__lowerCamelCase						)

  def get_encoder_layer_array(__lowerCamelCase     :	int    ,						__lowerCamelCase     :	str						):
    __UpperCAmelCase				:      List[Any]	 =    f"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
    __UpperCAmelCase				:      Union[str, Any]	 =    tf.train.load_variable(__lowerCamelCase    ,						__lowerCamelCase						)

    if "kernel" in name:
      __UpperCAmelCase				:      List[str]	 =    array.transpose()

    return torch.from_numpy(__lowerCamelCase						)

  def get_encoder_attention_layer_array(__lowerCamelCase     :	int    ,						__lowerCamelCase     :	str    ,						__lowerCamelCase     :	Optional[Any]						):
    __UpperCAmelCase				:      Union[str, Any]	 =    f"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
    __UpperCAmelCase				:      Tuple	 =    tf.train.load_variable(__lowerCamelCase    ,						__lowerCamelCase						)
    __UpperCAmelCase				:      List[Any]	 =    array.reshape(__lowerCamelCase						)

    if "kernel" in name:
      __UpperCAmelCase				:      Any	 =    array.transpose()

    return torch.from_numpy(__lowerCamelCase						)

  print(f"""Loading model based on config from {config_path}..."""						)
  __UpperCAmelCase				:      List[Any]	 =    BertConfig.from_json_file(__lowerCamelCase						)
  __UpperCAmelCase				:      List[Any]	 =    BertForMaskedLM(__lowerCamelCase						)

  # Layers
  for layer_index in range(0    ,						config.num_hidden_layers						):
    __UpperCAmelCase				:      BertLayer	 =    model.bert.encoder.layer[layer_index]

    # Self-attention
    __UpperCAmelCase				:      BertSelfAttention	 =    layer.attention.self

    __UpperCAmelCase				:      Union[str, Any]	 =    get_encoder_attention_layer_array(
        __lowerCamelCase    ,						"""_query_dense/kernel"""    ,						self_attn.query.weight.data.shape						)
    __UpperCAmelCase				:      List[Any]	 =    get_encoder_attention_layer_array(
        __lowerCamelCase    ,						"""_query_dense/bias"""    ,						self_attn.query.bias.data.shape						)
    __UpperCAmelCase				:      Any	 =    get_encoder_attention_layer_array(
        __lowerCamelCase    ,						"""_key_dense/kernel"""    ,						self_attn.key.weight.data.shape						)
    __UpperCAmelCase				:      int	 =    get_encoder_attention_layer_array(
        __lowerCamelCase    ,						"""_key_dense/bias"""    ,						self_attn.key.bias.data.shape						)
    __UpperCAmelCase				:      Any	 =    get_encoder_attention_layer_array(
        __lowerCamelCase    ,						"""_value_dense/kernel"""    ,						self_attn.value.weight.data.shape						)
    __UpperCAmelCase				:      int	 =    get_encoder_attention_layer_array(
        __lowerCamelCase    ,						"""_value_dense/bias"""    ,						self_attn.value.bias.data.shape						)

    # Self-attention Output
    __UpperCAmelCase				:      BertSelfOutput	 =    layer.attention.output

    __UpperCAmelCase				:      Optional[Any]	 =    get_encoder_attention_layer_array(
        __lowerCamelCase    ,						"""_output_dense/kernel"""    ,						self_output.dense.weight.data.shape						)
    __UpperCAmelCase				:      List[str]	 =    get_encoder_attention_layer_array(
        __lowerCamelCase    ,						"""_output_dense/bias"""    ,						self_output.dense.bias.data.shape						)

    __UpperCAmelCase				:      List[str]	 =    get_encoder_layer_array(__lowerCamelCase    ,						"""_attention_layer_norm/gamma"""						)
    __UpperCAmelCase				:      Optional[Any]	 =    get_encoder_layer_array(__lowerCamelCase    ,						"""_attention_layer_norm/beta"""						)

    # Intermediate
    __UpperCAmelCase				:      BertIntermediate	 =    layer.intermediate

    __UpperCAmelCase				:      int	 =    get_encoder_layer_array(__lowerCamelCase    ,						"""_intermediate_dense/kernel"""						)
    __UpperCAmelCase				:      Union[str, Any]	 =    get_encoder_layer_array(__lowerCamelCase    ,						"""_intermediate_dense/bias"""						)

    # Output
    __UpperCAmelCase				:      BertOutput	 =    layer.output

    __UpperCAmelCase				:      Optional[int]	 =    get_encoder_layer_array(__lowerCamelCase    ,						"""_output_dense/kernel"""						)
    __UpperCAmelCase				:      int	 =    get_encoder_layer_array(__lowerCamelCase    ,						"""_output_dense/bias"""						)

    __UpperCAmelCase				:      Dict	 =    get_encoder_layer_array(__lowerCamelCase    ,						"""_output_layer_norm/gamma"""						)
    __UpperCAmelCase				:      Dict	 =    get_encoder_layer_array(__lowerCamelCase    ,						"""_output_layer_norm/beta"""						)

  # Embeddings
  __UpperCAmelCase				:      Union[str, Any]	 =    get_encoder_array("""_position_embedding_layer/embeddings"""						)
  __UpperCAmelCase				:      Tuple	 =    get_encoder_array("""_type_embedding_layer/embeddings"""						)
  __UpperCAmelCase				:      List[str]	 =    get_encoder_array("""_embedding_norm_layer/gamma"""						)
  __UpperCAmelCase				:      Dict	 =    get_encoder_array("""_embedding_norm_layer/beta"""						)

  # LM Head
  __UpperCAmelCase				:      List[str]	 =    model.cls.predictions.transform

  __UpperCAmelCase				:      Tuple	 =    get_masked_lm_array("""dense/kernel"""						)
  __UpperCAmelCase				:      List[str]	 =    get_masked_lm_array("""dense/bias"""						)

  __UpperCAmelCase				:      Dict	 =    get_masked_lm_array("""layer_norm/gamma"""						)
  __UpperCAmelCase				:      Optional[Any]	 =    get_masked_lm_array("""layer_norm/beta"""						)

  __UpperCAmelCase				:      Optional[Any]	 =    get_masked_lm_array("""embedding_table"""						)

  # Pooling
  __UpperCAmelCase				:      Dict	 =    BertPooler(config=__lowerCamelCase						)
  __UpperCAmelCase				:      BertPooler	 =    get_encoder_array("""_pooler_layer/kernel"""						)
  __UpperCAmelCase				:      BertPooler	 =    get_encoder_array("""_pooler_layer/bias"""						)

  # Export final model
  model.save_pretrained(__lowerCamelCase						)

  # Integration test - should load without any errors ;)
  __UpperCAmelCase				:      List[str]	 =    BertForMaskedLM.from_pretrained(__lowerCamelCase						)
  print(new_model.eval()						)

  print("""Model conversion was done sucessfully!"""						)


if __name__ == "__main__":
       a						:			str     	=					argparse.ArgumentParser()
       parser.add_argument(
           "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow Token Dropping checkpoint path."
       )
       parser.add_argument(
           "--bert_config_file",
           type=str,
           required=True,
           help="The config json file corresponding to the BERT model. This specifies the model architecture.",
       )
       parser.add_argument(
           "--pytorch_dump_path",
           type=str,
           required=True,
           help="Path to the output PyTorch model.",
       )
       a						:			Union[str, Any]     	=					parser.parse_args()
       convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)



"""simple docstring"""



import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


SCREAMING_SNAKE_CASE__:Optional[Any]									=			logging.get_logger(__name__)






class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		lowerCamelCase						):
        super().__init__()
        __a       =    nn.ModuleList(lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = False    ,		lowerCamelCase = True    ,		):
        for i, (image, scale, controlnet) in enumerate(zip(lowerCamelCase    ,		lowerCamelCase    ,		self.nets						)						):
               __a ,   __a       =    controlnet(
                   lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		)

               # merge samples
               if i == 0:
                      __a ,   __a       =    down_samples, mid_sample
               else:
                      __a       =    [
                          samples_prev + samples_curr
                          for samples_prev, samples_curr in zip(lowerCamelCase    ,		lowerCamelCase						)
                      ]
                      mid_block_res_sample += mid_sample

        return down_block_res_samples, mid_block_res_sample


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		lowerCamelCase = False    ,		lowerCamelCase = None    ,		):
        __a       =    0
        __a       =    save_directory
        for controlnet in self.nets:
               controlnet.save_pretrained(
                   lowerCamelCase    ,		is_main_process=lowerCamelCase    ,		save_function=lowerCamelCase    ,		safe_serialization=lowerCamelCase    ,		variant=lowerCamelCase    ,		)

               idx += 1
               __a       =    model_path_to_save + F"_{idx}"






 @classmethod
 def 						a__   ( cls    ,		lowerCamelCase    ,		**lowerCamelCase						):
        __a       =    0
        __a       =    []

        # load controlnet and append to list until no controlnet directory exists anymore
        # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
        # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
        __a       =    pretrained_model_path
        while os.path.isdir(lowerCamelCase						):
               __a       =    ControlNetModel.from_pretrained(lowerCamelCase    ,		**lowerCamelCase						)
               controlnets.append(lowerCamelCase						)

               idx += 1
               __a       =    pretrained_model_path + F"_{idx}"

        logger.info(F"{len(lowerCamelCase						)} controlnets loaded from {pretrained_model_path}."						)

        if len(lowerCamelCase						) == 0:
               raise ValueError(
                   F"No ControlNets found under {os.path.dirname(lowerCamelCase						)}. Expected at least {pretrained_model_path + '_0'}."						)

        return cls(lowerCamelCase						)
